Interactive Machine Learning of Musical Gesture
This work addresses the challenge of applying machine learning to musical performance and sound synthesis for musicians and researchers in music technology.
This chapter explores Interactive Machine Learning (IML) for analyzing and designing musical gestures, focusing on capturing, analyzing, and applying IML to human bodily gestures for sound synthesis. It discusses using various algorithms, including Reinforcement Learning (RL) within an Assisted Interactive Machine Learning (AIML) paradigm, to interact with complex synthesis techniques and explore interaction possibilities.
This chapter presents an overview of Interactive Machine Learning (IML) techniques applied to the analysis and design of musical gestures. We go through the main challenges and needs related to capturing, analysing, and applying IML techniques to human bodily gestures with the purpose of performing with sound synthesis systems. We discuss how different algorithms may be used to accomplish different tasks, including interacting with complex synthesis techniques and exploring interaction possibilities by means of Reinforcement Learning (RL) in an interaction paradigm we developed called Assisted Interactive Machine Learning (AIML). We conclude the chapter with a description of how some of these techniques were employed by the authors for the development of four musical pieces, thus outlining the implications that IML have for musical practice.